research-article

Recommending Personalized News in Short User Sessions

Authors:

Elena Viorica Epure,

Benjamin Kille,

Jon Espen Ingvaldsen,

Rebecca Deneckere,

Camille Salinesi,

Sahin AlbayrakAuthors Info & Claims

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

Pages 121 - 129

https://doi.org/10.1145/3109859.3109894

Published: 27 August 2017 Publication History

Abstract

News organizations employ personalized recommenders to target news articles to specific readers and thus foster engagement. Existing approaches rely on extensive user profiles. However frequently possible, readers rarely authenticate themselves on news publishers' websites. This paper proposes an approach for such cases. It provides a basic degree of personalization while complying with the key characteristics of news recommendation including news popularity, recency, and the dynamics of reading behavior. We extend existing research on the dynamics of news reading behavior by focusing both on the progress of reading interests over time and their relations. Reading interests are considered in three levels: short-, medium-, and long-term. Combinations of these are evaluated in terms of added value to the recommendation's performance and ensured news variety. Experiments with 17-month worth of logs from a German news publisher show that most frequent relations between news reading interests are constant in time but their probabilities change. Recommendations based on combined short-term and long-term interests result in increased accuracy while recommendations based on combined short-term and medium-term interests yield higher news variety.

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Cited By

Bauer CBagchi CHundogan Ovan Es K(2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3654805
Vercoutere SJoris GDe Pessemier TMartens L(2024)Improving selection diversity using hybrid graph-based news recommendersUser Modeling and User-Adapted Interaction10.1007/s11257-024-09399-w34:4(955-993)Online publication date: 12-Jun-2024
https://doi.org/10.1007/s11257-024-09399-w
Kilani YSullabi MAlsarhan A(2023)Target-Based Versus Customer-Based Genetic Algorithms and Matrix Factorization Recommender Systems2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)10.1109/ICCNS58795.2023.10193537(223-229)Online publication date: 19-Jun-2023
https://doi.org/10.1109/ICCNS58795.2023.10193537
Show More Cited By

Index Terms

Recommending Personalized News in Short User Sessions
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Data stream mining
    2. Decision support systems
      1. Data analytics
  2. World Wide Web
    1. Web mining
      1. Web log analysis
    2. Web searching and information discovery
      1. Content ranking
      2. Personalization

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Published In

cover image ACM Conferences

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

August 2017

466 pages

ISBN:9781450346528

DOI:10.1145/3109859

General Chairs:
Paolo Cremonesi
Politecnico di Milano, Italy
,
Francesco Ricci
Free University Bozen-Bolzano, Italy
,
Program Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Alexander Tuzhilin
New York University, USA

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2017

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RecSys '17

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SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

Acceptance Rates

RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

Upcoming Conference

RecSys '24

Sponsor:
sigchi

18th ACM Conference on Recommender Systems

October 14 - 18, 2024

Bari , Italy

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Cited By

Bauer CBagchi CHundogan Ovan Es K(2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3654805
Vercoutere SJoris GDe Pessemier TMartens L(2024)Improving selection diversity using hybrid graph-based news recommendersUser Modeling and User-Adapted Interaction10.1007/s11257-024-09399-w34:4(955-993)Online publication date: 12-Jun-2024
https://doi.org/10.1007/s11257-024-09399-w
Kilani YSullabi MAlsarhan A(2023)Target-Based Versus Customer-Based Genetic Algorithms and Matrix Factorization Recommender Systems2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)10.1109/ICCNS58795.2023.10193537(223-229)Online publication date: 19-Jun-2023
https://doi.org/10.1109/ICCNS58795.2023.10193537
Idhaim HKilani YAlsarhan AAljaidi MAltaweel ABouridane AAldweesh A(2023)Improving Recommender Systems by a Further Factorization of the Factor MatricesIEEE Access10.1109/ACCESS.2023.330848911(91539-91549)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3308489
Alabduljabbar RAlmazrou HAldawod A(2023)Context-Aware News Recommendation System: Incorporating Contextual Information and Collaborative Filtering TechniquesInternational Journal of Computational Intelligence Systems10.1007/s44196-023-00315-516:1Online publication date: 23-Aug-2023
https://doi.org/10.1007/s44196-023-00315-5
Karimi MCule BGoethals B(2023)Leveraging Sequential Episode Mining for Session-Based News RecommendationWeb Information Systems Engineering – WISE 202310.1007/978-981-99-7254-8_46(594-608)Online publication date: 21-Oct-2023
https://doi.org/10.1007/978-981-99-7254-8_46
Symeonidis PKirjackaja LZanker M(2023)Session-Based Recommendation Along with the Session Style of ExplanationMachine Learning and Knowledge Discovery in Databases10.1007/978-3-031-26387-3_25(404-420)Online publication date: 17-Mar-2023
https://doi.org/10.1007/978-3-031-26387-3_25
Gong SZhu KAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Positive, Negative and NeutralProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532040(1185-1195)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532040
Sheu HChu ZQi DLi S(2022)Knowledge-Guided Article Embedding Refinement for Session-Based News RecommendationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.308495833:12(7921-7927)Online publication date: Dec-2022
https://doi.org/10.1109/TNNLS.2021.3084958
Schnauber-Stockmann AScharkow MBreuer J(2022)Routines and the Predictability of Day-to-Day Web UseMedia Psychology10.1080/15213269.2022.212128626:3(229-251)Online publication date: 6-Sep-2022
https://doi.org/10.1080/15213269.2022.2121286
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